Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Different global transpiration products exhibit significant differences, necessitating the evaluation of errors. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome of this research is a long-term daily global transpiration dataset at 0.1° resolution from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with input datasets. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

To effectively reduce CO2 emissions, it’s vital to identify and quantify their sources. While the focus has been on CO2 from fossil fuel combustion, especially coal, the CO2 produced from coal’s other elements, such as sulfur, through chemical reaction, remains an ‘invisible’ carbon source. We analyzed the invisible carbon flux due to coal burning in the Xijiang River Basin, a highly industrialized region in China, using river sulfate fluxes. Dissolved sulfate concentration in the Xijiang River rose by over 300% from 1985 to 2011, largely due to coal combustion. In 2011, this resulted in 3.14 Mt of invisible carbon dioxide. We evaluated the impact of two flue gas desulfurization (FGD) methods on carbon emissions using a predictive model. By enhancing SO2 removal efficiency through these methods, China could cut invisible carbon emissions by 27.8 Mt CO2 annually, paving the way for a sustainable future.

A document by Hongxiang Yan. Click on the document to view its contents.

This paper presents the first application of multichannel singular spectrum analysis (M-SSA) to radar satellite geodesy. We apply M-SSA to Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) time series processed for Pacaya Volcano in Guatemala in two steps. First, we produce, in an iterative and data-adaptive way, estimates of missing data points to obtain evenly sampled time series. The resulting time series are then decomposed with M-SSA into long-periodic nonlinear trends and oscillatory modes providing a sparse representation of the signals present in the data. The M-SSA approach presented herein is designed to deal with very large datasets such as collections of InSAR time series. Combining M-SSA with power spectrum analysis show that the dominant frequencies of the main oscillatory modes correspond to 1, 1.5, 2, 3, 5.8 and 6.8 cycle per years. These frequencies are consistent with the seasonal variability of the regional hydrological system, as determined from correlograms of rainfall time series and M-SSA modes extracted from time series of regional gravity anomalies using Gravity Recovery and Climate Experiment (GRACE) data, Global Navigation Satellite Systems (GNSS) time series recorded in Guatemala City, and phase delay maps derived from a global weather model. While some of the seasonal oscillations correlate well with topography, others show significant spatial asymmetries. The extracted nonlinear trends show large amplitudes around the summit and within the area covered by the 2014 lava flows and, to a lesser extent, the 2010 lava flows. This nonlinear trend correlates with interannual variability of the regional water cycle.

In sparsely fractured crystalline rock, aperture variability exhibits significant control of the flow field through the fracture network. However, its inclusion in models is hampered due to a lack of field measurements and adequate numerical representation. A model for aperture generation is developed based on self-affine methods which includes two key parameters, the Hurst exponent and a scaling parameter, and which accounts for relative anisotropy and correlation between the adjacent surfaces forming the fracture. A methodology for analysing and extracting the necessary parameters from 3D surface scans of natural rock fractures is also developed. Analysis of the Hurst exponent and scaling parameter space shows that input combinations following a linear upper bound can be used to generate aperture fields which accurately reproduce measurements. It is also shown that the Hurst and scaling parameters are more sensitive than the correlation between the upper and lower fracture surfaces. The new model can produce an aperture ensemble that closely corresponds with the aperture obtained from the surface scans, and is an improvement on previous methods. The model is also successfully used to up-scale fracture apertures based on measurements restricted to a small sub-section of the sample. Thereby, the aperture fields generated using the model are representative of natural fracture apertures and can be implemented in larger scale fracture network models, allowing for numerical simulations to included representation of aperture internal heterogeneity.

Convergent coastal-plain estuaries have been shortened by dam-like structures worldwide. We used 31 long-term water level stations and a semi-analytical tide model to investigate the influence of a dam and landward-funneling on tides and storm surge propagation in the greater Charleston Harbor region, South Carolina, where three rivers meet: the Ashley, Cooper, and Wando. Our analysis shows that the principle tidal harmonic (M2), storm surge, and long-period setup-setdown (~4–10 days) propagate as long waves with the greatest amplification and celerity observed in the M2 wave. All waves attenuate in landward regions, but, as they approach the dam on the Cooper River, a frequency dependent response in amplitude and phase progression occurs. Dam-induced amplification scales with wave frequency, causing the greatest amplification in M2 overtides. Model results show that funneling and the presence of a dam amplify tidal waves through partial and full reflection, respectively. The different phase progression of these reflected waves, however, can ultimately reduce the total wave amplification. We use a friction-convergence parameter space to demonstrate how amplification is largest for partial reflection, when funneling and wave periods are not extreme (often the case of dominant tides), and for full reflection, when funneling and/or wave periods are small. The analysis also shows that in the case of long period events (>day), such as storm surges, dams may attenuate the wave in funneling estuaries. However, dams may amplify the most intense storm surges (short, high) more than funneling with unexpected consequence that can greatly increase flood exposure.

Subsurface tidal analysis requires only continuous pressure monitoring data and therefore can be a cost-effective technique for estimating aquifer properties. The tidal behavior of a well in a semiconfined aquifer can be described by a diffusion equation that includes a leakage term. This approach is valid for thin aquifers, as long as the overlying layer has low permeability relative to the main aquifer. However, in cases where the aquifer is not thin and the permeability of the overlying layer is not low, using the existing solutions based on these approximations may lead to unsatisfactory outcomes. Alternative solutions for both vertical and horizontal wells were obtained by solving the standard diffusion equation, with leakage expressed as a boundary condition. Furthermore, a nondimensional number was derived mathematically, which forms the basis for a quantitative criterion to assess the applicability of the existing solutions. In the case of a vertical well, the existing solution exhibits acceptable error only if the nondimensional number is less than 0.245. Our new solution extends this upper limitation to 0.475. However, when the number is greater than 0.475, both the existing solution and our new solution are invalid due to the invalid uniform flowrate assumption. For a horizontal well, when the number is less than 0.245, the existing solution is suitable with acceptable error. Our new solution effectively overcomes this limitation. Finally, the new solution was applied to the case of the Arbuckle aquifer to demonstrate the improved validity of the new solution compared to the existing one.

We analyzed observations on floods in rivers located in Finland and northern Russia where hazardous floods often happen during a spring flooding period. We evaluated the length of spring flooding periods, the volume of spring floods, the yearly maximum water discharges (annual floods) and their dates from hydrographs. The hydrographs were evaluated using the daily water discharges given in yearly books published by the national hydrological services. The long term time series of annual and spring floods were used to define shifts (step changes) by applying the moving window technique. Three statistical criteria namely the Student test, the Kolmogorov-Smirnov test and the Mann-Whitney test were used. Our results suggest that the annual floods were recorded in the spring flooding period in more than 85 % of the rivers selected. In the last two decades, the number of annual floods that happened in autumn-winter season increased almost twice in the southern Finnish rivers. The melting snow remains the dominant source for the highest floods in the rivers located in northern Finland and Russia. The step changes were defined in half of the time series of the annual floods and spring floods. In over a one-third of the records of the spring floods, the step changes dated to the late 1990s, since then the volume of floods increased by 21 % on average. The step changes in the records of the annual floods dated to the early 1950s, mid 1970s and early 1990s.

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two-thirds of flood-exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program (NFIP). We study whether repeated exposure to flood events, especially disaster-scale floods expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take-up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take-up rate of insurance by 7% in the following year, but the effect diminishes in subsequent years and is gone after five years. This effect is more short-lived in counties in inland states that do not border the Gulf and Atlantic coasts. The effect of a flood on takeup is substantially larger if there was also a flood in the previous year. We also find that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non-primary residences. Our results provide a more comprehensive understanding of the salience effect of flooding on insurance demand compared to previous studies. Overall, these findings suggest that relying on households to self-adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap.

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

Some recent land surface models can explicitly represent land surface process and focus more on sub-grid terrestrial features. Many studies have involved the analysis of how hillslope water dynamics determine vegetation patterns and shape ecologically and hydrologically important landscapes, such as desert riparian and waterlogged areas. However, the global locations and abundance of hillslope-dominated landscapes remain unclear. To address this knowledge gap, we propose a globally applicable method that employs high-resolution elevation, hydrography, and land cover data to neatly resolve explicit land cover heterogeneity for the mapping of hillslope-dominated landscapes. First, we aggregate pixels into unit catchments to represent topography-based hydrological units, and then vertically discretize them into height bands to approximate the hillslope profile. The dominant land cover type in each height band is determined, and the uphill land cover transition is analyzed to identify hillslope-dominated landscapes. The results indicate that hillslope-dominated landscapes are distributed extensively worldwide in diverse climate zones. Notably, some landscapes, including gallery forests in northeastern Russia and desert riparian in the Horn of Africa, are newly revealed. Furthermore, the proposed strategy enables more accurate representation of explicit land cover heterogeneity than does the simple downscaling of a rectangular grid from larger to smaller units, revealing its capability to neatly resolve land cover heterogeneity in land surface modeling with relatively high accuracy. Overall, we present the extensive global distribution of landscapes shaped by hillslope water dynamics, underscoring the importance of the explicit resolution of heterogeneity in land surface modeling.

Saturated hydraulic conductivity (Ks) is a crucial parameter that influences water flow in saturated soils, with applications in various fields such as surface water runoff, soil erosion, drainage, and solute transport. However, accurate estimation of Ks is challenging due to temporal and spatial uncertainties. This study addresses the knowledge gap regarding the long-term behaviour of Ks in sandy soils with less than 10% fine particles. The research investigates the changes in Ks over a long period of constant head tests and examines the factors influencing its variation. Two sandy samples were tested using a hydraulic conductivity cell, and the hydraulic head and discharge were recorded for over 50 days. The results show a general decline in Ks throughout the test, except for brief periods of increase. Furthermore, the relationship between flow rate and hydraulic head gradient does not follow the expected linear correlation from Darcy’s law, highlighting the complex nature of sandy soil hydraulic conductivity. The investigation of soil properties in three different sections of the samples before and after the tests revealed a decrease in the percentage of fine particles and a shift in specific gravity from the bottom to the top of the sample, suggesting particle migration along the flow direction. Factors such as clogging by fine particles and pore pressure variation contribute to the changes in Ks. The implications of this study have far-reaching effects on various geotechnical engineering applications. These include groundwater remediation, geotechnical stability analysis, and drainage system design.

Changed hydrological regimes, sea-level rise, and accelerated subsidence are all putting river deltas at risk across the globe. Deltas may respond to these stressors through the mechanism of avulsion. Decades of delta avulsion studies have resulted in conflicting hypotheses that avulsion frequency and location are upstream (water and sediment discharge) or downstream (backwater and sea-level rise) controlled. In this study, we use Delft3D morphodynamic simulations to investigate the main controls over delta avulsion. Avulsion timing and location were recorded in six scenarios modelled over a 400-year period with varying alluvial slopes upstream of a delta slope break (1.13x10-4 to 3.04x10-3) within a range representative global deltas. We measure several independent morphometric variables including avulsion length, delta lobe width, channel width at avulsion, delta topset slope and sediment load. Correlating these variables with the avulsion timescales observed in our model shows that avulsion timescale is mostly controlled by sediment load, which in turn is controlled by the alluvial slope upstream of a delta slope break. With higher stream power index in steeper alluvial slopes, more sediment can be carried within a channel, resulting in more frequent avulsions. Our results are consistent with the avulsion timescale derived from an analytical solution, 19 natural deltas and downscaled physical laboratory deltas. These results help mitigate delta avulsion risk by focusing management efforts on variables that primarily control avulsion in a river delta, but also induce further debate over whether sea-level rise may, or may not, trigger more avulsions in river deltas.

Interactions among atmospheric, root-soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub-daily timescales and validate process-based and data-driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory-based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower datasets in the Midwestern U.S. in intensively managed corn-soybean landscapes. We compare Multiple Linear Regressions (MLR), Long-Short Term Memory (LSTM), and Random Forests (RF) to evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive performance and functional performance, which examines the full suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance. Regionally trained models demonstrate lower predictive but higher functional performance compared to site-specific models, suggesting superior reproduction of observed relationships. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models.

The cumulative and bidirectional groundwater-surface water (GW-SW) interaction along a stream is defined as hydrological turnover (HT) influencing solute transport and source water composition. However, HT proves to be highly variable, producing spatial exchange patterns influenced by local surface- and groundwater levels, geology and topography. Hence, identifying factors controlling HT in streams poses a challenge. We studied the spatiotemporal variability of HT processes at a third order tributary of the river Mosel, Germany at two different stream reaches over a period of two years. Additionally, we sampled for silicate concentrations in the stream as well as in the near-stream groundwater. Thus, creating snapshots of the boundary layer between ground- and surface water where turnover induced mixing occurs. We characterize reach specific drainage behavior by utilizing a delayed/base flow separation analysis for both reaches. The results show a site-specific negative correlation of HT with discharge, while hydraulic gradients and reach scale absolute discharge changes correlating with HT only at the upstream site which is characterized by steeper hillslopes compared to the downstream section. Analyzing the variation of silicate concentrations between stream and wells shows that in-reach silicate variation increases significantly with the decrease of HT under groundwater dominated flow conditions.. In Summary, our results show that discharge shapes the influence of HT on solute transport as visualized by silicate variations. Yet, reach specific drainage behavior shapes seasonal states of groundwater storages and thus, can be an additional control of HT magnitudes, influencing physical stream water composition throughout the year.

To estimate groundwater flow and transport, lumped conceptual models are widely used due to their simplicity and parsimony - but these models are calibration reliant as their parameters are unquantifiable through measurements. To eliminate this inconvenience, we tried to express these conceptual parameters in terms of hydrodynamic aquifer properties to give lumped models a forward modelling potential. The most generic form of a lumped model representing groundwater is a unit consisting of a linear reservoir connected to a dead storage aiding extra dilution, or a combination of several such units mixing in calibrated fractions. We used one such standard two-store model as our test model, which was previously nicely calibrated on the groundwater flow and transport behaviour of a French agricultural catchment. Then using a standard finite element code, we generated synthetic Dupuit-Forchheimer box aquifers and calibrated their hydrodynamic parameters to exactly match the test model’s behaviour (concentration, age etc). The optimized aquifer parameters were then compared with conceptual parameters to find clear physical equivalence and mathematical correlation - we observed that the recession behaviour depends on the conductivity, fillable porosity, and length of the catchment whereas the mixing behaviour depends on the total porosity and mean aquifer thickness. We also noticed that for a two-store lumped model, faster and slower store represents differences only in porosities making it rather a dual porosity system. We ended with outlining a clear technique on using lumped models to run forward simulations in ungauged catchments where valid measurements of hydrodynamic parameters are available.

Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, Soil Moisture is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining vapor pressure deficit (VPD) with soil moisture information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Index (SPI) for six major drought events across the United States including three flash drought events and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPI for flash droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both flash and conventional drought events. The results also show that combining VPD with soil moisture reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and soil moisture information.

A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To that end, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

The mechanism of sandbars initiation and formation is unresolved. The occurrence of sandbars has been investigated using stability analysis, which assumes that sandbars occur due to the inherent instability of a riverbed. However, there are no data, either from riverine observations or model experiments, to support this assumption. Here, we conducted flume experiments in which sandbars were formed from a flatbed by simultaneously measuring the water surface and bottom surface. The results showed that the process of sandbars initiation and formation first involves the generation of small periodic bedforms; then, the bedforms transition to small three-dimensionally shaped rhomboid bars, and finally the rhomboid bars transition to sandbars. The measurements also suggested that wave trains occurred on the water surface. We then conducted fixed-bed experiments under the same conditions as a moving bed to ascertain the behavior of the water surface. The results of these fixed-bed experiments showed that standing waves were observed on the water surface even when the experimental conditions were steady and the flatbed channel was straight. A two-dimensional wavenumber analysis showed that the dominant wavenumbers of the standing waves and initial small bedforms were in good agreement. The whole set of results indicated that standing waves were already present on the water surface before bedforms occurred and that one of the factors in sandbars initiation was the presence of standing waves on the water surface.

Nocturnal water use (Qnight) is an important component of the eucalyptus water budget, but it has always been under-appreciated and poorly understood. To improve the accuracy of water balance estimates and understanding of the nocturnal water use process in eucalypts plantations, we conducted a 3-year study to investigate the characteristics of Qnight and its components in a Eucalyptus urophylla × E.grandis plantation in southern China. The results showed that the Qnight of E.urophylla × E.grandis was substantial and its contribution (Rnight) to daily water use (Qdaily) was on average 12.35%, with higher Rnight (14.97%) in the dry season than in the wet season (9.50%). However, the Qnight was used not only for nocturnal transpiration (Tn), but also for stem refilling (Re). Tn was influenced by a combination of vapor pressure deficit (VPD), air temperature (Ta) and relative humidity (RH), with VPD being the dominant driver. Based on this, combined with the fact that Re was closely related to diurnal variations in diameter, we have developed a novel method to distinguish Tn from Re. We found that the compositional ratios of Tn and Re differed between weather conditions and months. However, on a 3-year average, Qnight of E.urophylla × E.grandis was still mainly used for Tn (58.63%). Our results highlight the non-ignorability of Qnight and the high variability of the compositional ratios of Re and Tn, and suggest that Qnight and its components should be accurately quantified and taken into account when studying the water balance in eucalyptus stands.

Rapidly changing climate is disrupting the High Arctic’s natural water systems. This disruption demands high quality monitoring of Arctic hydrology to better reconstruct past changes, track ongoing transformations, and assess future environmental threats. Water isotopes are valuable tracers of hydrological processes, but logistical challenges limit the length and scope of isotopic monitoring in High Arctic landscapes. Here, we present a comprehensive isotopic survey of 535 water samples taken in 2018–2019 of the lakes, streams, and other surface waters of the periglacial Pituffik Peninsula in far northwest Greenland. The δ18O, δ2H, and deuterium-excess values of these samples, representing 196 unique sites, grant us unprecedented insight into the environmental drivers of the region’s hydrology and water isotopic variability. We find that the spatial and temporal variability of lake isotopes is dominated by evaporation and connectivity to summer meltwater sources, while evaporation determines interannual isotopic changes. Stream isotopic compositions vary in both space and time based on the relative source balance of tundra snowpack meltwater versus surface melt from the nearby Greenland Ice Sheet. Overall, our survey highlights the diversity of isotopic composition and evolution in Pituffik surface waters, and our complete isotopic and geospatial database provides a strong foundation for future researchers to study hydrological changes at Pituffik and across the Arctic. Water isotope samples taken at individual times or sites in similar periglacial landscapes likely have limited regional representativeness, and increasing the spatiotemporal extent of isotopic sampling is critical to producing accurate and informative High Arctic paleoclimate reconstructions.

With the rising air temperature and precipitation, water and sediment flux in the Source Region of the Yangtze River have increased significantly since 2000. Nonetheless, the response of braided river morphology to climate-driven water and sediment flux change is still unknown. Water bodies of nine large braided rivers from 1990 to 2020 were extracted based on Google Earth Engine platform, and impacts of climate change on activation indices of braided river morphology were quantified. The main results are presented that a new method of braided water body extraction by combining Lowpath algorithm and Local Otsu algorithm is firstly proposed, which reduces 59% of the root mean squared error of braiding intensity in comparison with the Global Otsu method. The braiding intensity has a parabolic variation trend with the water area ratio, and the average sandbar area ratio has a negative power law trend with the water area ratio. Intra-annual channel migration intensity has an obvious temporal scale effect, which increases rapidly when the time span is less than 5 years. The warming and wetting trend led to vegetation cover increasing significantly. With the increase of runoff, water area of each braided reach has increased in both flood and non-flood season. Intra-annual channel migration intensity shows three different trends of increasing, weakening, and unchanged over time. The response of migration intensity to climate warming can be classified into three patterns in the SRYR as follows: sediment increase constrained pattern, sediment increase dominated pattern, and runoff increase dominated pattern.

Preprint, August 28th 2023Authors: Paolo Scussolini1*, Linh N. Luu2,3,4, Sjoukje Y. Philip4, Wouter R. Berghuijs5, Dirk Eilander1,6, Jeroen C.J.H. Aerts1, Sarah F. Kew4, Geert Jan van Oldenborgh4† , Willem H.J. Toonen5, Jan Volkholz7, Dim Coumou1,41Institute for Environmental Studies, Vrije Universiteit Amsterdam, Netherlands2Department of Geography, University of Lincoln, United Kingdom3Vietnam Institute of Meteorology, Hydrology and Climate Change, Hanoi, Vietnam4Royal Netherlands Meteorological Institute, Netherlands5Department of Earth Science, Vrije Universiteit Amsterdam, Netherlands6Deltares, Netherlands7Potsdam Institute for Climate Impact Research, Germany*Corresponding author: [email protected]† Deceased, October 12th2021